8 citations · 20 across the 10 of their papers we have counts for
8 papers · 1 filter
Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions
Sehwan Kim, Yan Sun, Faming Liang
Over the past decade, deep neural networks (DNNs) have achieved remarkable success on complex machine-learning tasks, yet the theoretical foundations of their performance remain in…
Uncertainty Quantification for Large-Scale Deep Networks via Post-StoNet Modeling
Yan Sun, Faming Liang
Deep learning has revolutionized modern data science. However, how to accurately quantify the uncertainty of predictions from large-scale deep neural networks (DNNs) remains an unr…
Statistical Inference for Generative Model Comparison
Zijun Gao, Yan Sun, Han Su
Generative models have achieved remarkable success across a range of applications, yet their evaluation still lacks principled uncertainty quantification. In this paper, we develop…
Magnitude Pruning of Large Pretrained Transformer Models with a Mixture Gaussian Prior
Mingxuan Zhang, Yan Sun, Faming Liang
Large pretrained transformer models have revolutionized modern AI applications with their state-of-the-art performance in natural language processing (NLP). However, their substant…
Extended Fiducial Inference: Toward an Automated Process of Statistical Inference
Faming Liang, Sehwan Kim, Yan Sun
While fiducial inference was widely considered a big blunder by R.A. Fisher, the goal he initially set --`inferring the uncertainty of model parameters on the basis of observations…
Sparse Deep Learning for Time Series Data: Theory and Applications
Mingxuan Zhang, Yan Sun, Faming Liang
Sparse deep learning has become a popular technique for improving the performance of deep neural networks in areas such as uncertainty quantification, variable selection, and large…